Precise constraints on the dark matter content of Milky Way dwarf galaxies for gamma-ray experiments

By L. Strigari, S. Koushiappas, James S. Bullock, Manoj Kaplinghat UC-Irvine, Lanl. 2006

160 citations recorded by the source corpus.

We examine the prospects for detecting {gamma}-rays from dark matter annihilation in the six most promising dwarf spheroidal (dSph) satellite galaxies of the Milky Way. We use recently measured velocity dispersion profiles to provide a systematic investigation of the dark matter mass distribution of each galaxy, and show that the uncertainty in the {gamma}-ray flux from mass modeling is less than a factor of {approx}5 for each dSph if we assume a smooth Navarro-Frenk-White (NFW) profile. We show that Ursa Minor and Draco are the most promising dSphs for {gamma}-ray detection with GLAST and other planned observatories. For each dSph, we investigate the flux enhancement resulting from halo substructure, and show that the enhancement factor relative to a smooth halo flux cannot be greater than about 100. This enhancement depends very weakly on the lower mass cutoff scale of the substructure mass function. While the amplitude of the expected flux from each dSph depends sensitively on the dark matter model, we show that the flux ratios between the six Sphs are known to within a factor of about 10. The flux ratios are also relatively insensitive to the current theoretical range of cold dark matter halo central slopes and substructuremore » fractions.« less

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/9af03fee2222bd5c01c7f8fa6ef12d0635fae3ed

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Citations in the corpus

DWARF GALAXY ANNIHILATION AND DECAY EMISSION PROFILES FOR DARK MATTER EXPERIMENTS (2014, background): “…are assumed to follow an analytic form that is typically used to describe low-mass “subhalos” (virial mass ∼ 109−10M ) that form around Milky-Way-like galaxies in dissipationless cosmological simulations based on cold dark matter (e.g. Strigari et al. 2007; Martinez et al. 2009; Martinez 2013).”
Disruption of Dark Matter Substructure: Fact or Fiction? (2017, background): “Finally, dark matter substructure boosts the expected dark matter annihilation signal (Bergström et al. 1999), an effect that is typically quantified in terms of a ‘boost-factor’ (e.g., Strigari et al. 2007; Giocoli et al. 2008b; Kuhlen et al. 2008; Pieri et al. 2008; Moliné et al. 2016).”
The flattening of the concentration–mass relation towards low halo masses and its implications for the annihilation signal boost (2013, background): “We define the boost B(M) as follows (Strigari et al. 2007; Kuhlen et al. 2008): B(M) = 1 L(M) ∫ M Mmin (dN/dm) [1 +B(m)] L(m) dm, (2) where L(M) = 4πMc3/f(c)2 is the halo annihilation luminosity with no substructures, c being the concentration, f(c) = log(1+c)−1/(1+c), and dN/dm = A/M (m/M)−α is…”
Dark matter annihilation and decay in dwarf spheroidal galaxies: the classical and ultrafaint dSphs (2015, methodology): “This technique has already been widely applied to dSphs (Strigari et al. 2007; Essig et al. 2010; Charbonnier et al. 2011; Geringer-Sameth, Koushiappas & Walker 2015).”
Characterization of subhalo structural properties and implications for dark matter annihilation signals (2016, methodology): “The most massive subhalos host low-luminosity dwarf galaxies, so by studying the velocity dispersion profiles of their stars, the underlying DM distribution and thus, the subhalo internal structure, could be inferred (Lokas et al. 2005; Strigari et al. 2007; Martinez et al. 2009).”
Redefining the Missing Satellites Problem (2007, methodology): “We instead use the fact that the total cumulative mass within a characteristic radius ∼ 0.6 kpc is much better determined by the present data (Strigari et al. 2007).”
Gamma Ray Signals from Dark Matter: Concepts, Status and Prospects (2012, result): “e [124, 125]. S igniﬁcant substructure boosts seem unlikely [124, 125] though it is has been speculated that they might enhance the signal by up to two orders of magnitude in the most optimistic case [129]. This situation is in sharp contrast to the large uncertainties related to the signal ﬂux from”
Galactic Searches for Dark Matter (2012, methodology): “Several analysis methods have shown that the line-of-sight velocity data sets constrain the integrated dark matter mass within the approximate half-light radius, independent of the parameterization of the velocity anisotropy (Strigari et al., 2007a; Walker et al., 2009; Wolf et al., 2010).”
Optimized dark matter searches in deep observations of Segue 1 with MAGIC (2013, background): “Furthermore, all of the results are produced without the assumptions of some additional boosts, either from the presence of substructures [44] or from quantum effects [45].”
SEARCH FOR DARK MATTER SATELLITES USING FERMI-LAT (2012, unknown): “…of a satellite can be characterized by the parameter ao Ts/ D. Approximately 90% of the integrated J-factor comes from within the angular radius ao (Strigari et al. 2007) and the LAT is sensitive to the spatial extension of satellites with a > 0.5 We used the likelihood ratio test, as implemented…”
The Aquarius Project: the subhaloes of galactic haloes (2008, background): “The density profile of subhalos also determines the kinematics of the stars in satellite galaxies, which are observationally accessible for the dwarf spheroidals around the Milky Way (e.g. Stoehr et al., 2002; Strigari et al., 2007a).”
Numerical simulations of the dark universe: State of the art and the next decade (2012, methodology): “9 [44, 45], and to use an analytical approach to determine the subhalo luminosity-mass relation down to the smallest mass halos [90, 91, 137].”
The Most Dark-Matter-dominated Galaxies: Predicted Gamma-Ray Signals from the Faintest Milky Way Dwarfs (2007, methodology): “As was shown in Strigari et al. (2007b), the boost factor cannot attain arbitrarily large values, but instead is bounded to be less than 100, with the exact value depending on the cutoff scale in the CDM mass function.”
Dark Matter Substructure in Numerical Simulations: A Tale of Discreteness Noise, Runaway Instabilities, and Artificial Disruption (2018, background): “Dark matter substructure also boosts the expected dark matter annihilation signal (Bergström et al. 1999), an effect that is typically quantified in terms of a ‘boost-factor’ (e.g., Strigari et al. 2007; Giocoli et al. 2008b; Kuhlen et al. 2008; Pieri et al. 2008; Moliné et al. 2016).”
The Dark Matter Annihilation Signal from Galactic Substructure: Predictions for GLAST (2008, methodology): “Our analytic boost factor model is based on the one presented in Strigari et al. (2007a).”